Identification of novel inhibitors of Pseudomonas aeruginosa MurC enzyme derived from phage-displayed peptide libraries
Bibliographic record
Abstract
OBJECTIVES: The machinery of peptidoglycan biosynthesis is an ideal site at which to look for novel antimicrobial targets. Phage display was used to develop novel peptide inhibitors for MurC, an essential enzyme involved in the early steps of biosynthesis of peptidoglycan monomer. METHODS: We cloned and overexpressed the murA, -B and -C genes from Pseudomonas aeruginosa in the pET expression vector, adding a His-tag to their C termini. The three proteins were overproduced in Escherichia coli and purified to homogeneity in milligram quantities. MurA and -B were combinatorially used to synthesize the MurC substrate UDP-N-acetylmuramate, the identity of which was confirmed by mass spectrometry and nuclear magnetic resonance analysis. Two phage-display libraries were screened against MurC in order to identify peptide ligands to the enzyme. RESULTS: Three rounds of biopanning were carried out, successively increasing elution specificity from round 1 to 3. The third round was accomplished with both non-specific elution and competitive elution with each of the three MurC substrates, UDP-N-acetylmuramic acid (UNAM), ATP and L-alanine. The DNA of 10 phage, selected randomly from each group, was extracted and sequenced, and consensus peptide sequences were elucidated. Peptides were synthesized and tested for inhibition of the MurC-catalysed reaction, and two peptides were shown to be inhibitors of MurC activity with IC(50)s of 1.5 and 0.9 mM, respectively. CONCLUSION: The powerful selection technique of phage display allowed us to identify two peptide inhibitors of the essential bacterial enzyme MurC. The peptide sequences represent the basis for the synthesis of inhibitory peptidomimetic molecules.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".